Safety assessment of coordinated signalized intersections in a connected vehicle environment: a microsimulation approach.

In this study, the safety impact of the coupled implementation of signal coordination and connected vehicles (CVs) is examined in a microsimulation environment created in VISSIM. The Surrogate Safety Assessment Model (SSAM) was implemented to generate results of surrogate safety measures. The findings provided evidence that CVs can improve the safety performance at all market penetration rates (MPRs) of CVs in terms of all performance metrics. In addition, further safety improvements were achieved at higher CV MPRs. It was observed that coordinated signals had lower likelihoods of experiencing collisions compared to uncoordinated signals. Specifically, coordinated signals showed significantly higher time-to-collision (TTC) and post-encroachment time (PET) values when compared to uncoordinated signals at the 100% CV MPR only. Moreover, the impact of CV technologies on reducing the total number of conflicts (TNC) would be stronger than that of traffic signal coordination alone while both would lead to reductions in the TNC.

Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach.

For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007-2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%-42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.

Assessment of tire failure related crashes and injury severity on a mountainous freeway: Bayesian binary logit approach.

Although tires maintain the only contact between the vehicle and the ground, tire failures are still underrepresented in traffic safety assessments. Vehicle stability and safety can deteriorate significantly by a sudden tire failure. The current body of literature on tire failure-related crashes is limited, and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity. The contributions of this study include (i) investigating the factors affecting tire failures, (ii) assessing the impacts of tire failures on occupant injury severity, and (iii) demonstrating the necessity of statewide tire inspection regulations. An extensive exploratory analysis was performed using ten years (2007-2016) of historical crash data along I-80 in Wyoming. Binary logistic regression with the Bayesian inference approach was applied to develop two separate models: tire failure and injury severity model. The results from the tire failure model showed that vehicle speeds greater than 75 mph, commercial motor vehicles, summer season, daytime, the presence of rough surface, downgrades, and concrete pavement are all related to higher tire failure occurrences. On the other hand, the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances: fire or explosion, rollover, guardrail hits, runoff road, angle, rear-end, clear weather, speeding, downgrades, and curved segments. With the incorporation of the random intercept in the modeling procedure, the injury severity analysis found a strong presence (42 %) of intra-crash correlation (effects of the common crash-specific unobserved factors) in occupant injury severity within the same crash. Finally, based on the findings of the study, recommendations are provided to alleviate tire-related problems.

Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach.

Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007-2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.